robust gradientsampling algorithm for nonsmooth, nonconvex optimization The authors describe a practical and robust ... only request formulated is that the gradient of the function is easily computed where...

package based on the BFGS and gradientsampling methods. For general unconstrained minimization: convex ... including BFGS, limited memory BFGS and gradientsampling methods, based on weak Wolfe line search...

interpolates to get an approximation of the gradient. Implicit Filtering describes the algorithm, its convergence ... area of derivative-free or sampling methods to be accompanied by publicly available software...

provide gradient information. If analytic gradients of the objective or constraint functions are not available ... used without special knowledge of optimization techniques. Sample problems are inc! luded to help...

such as the quadratic subproblem solution, the gradient updating, the working set selection, are systematically ... real-world data sets with millions training samples highlight how the software makes large scale...

NESVM: A Fast Gradient Method for Support Vector Machines. Support vector machines (SVMs) are invaluable ... applications with a great deal of samples as well as a large number of features ... paper, thus, we present NESVM, a fast gradient SVM solver that can optimize various...

dual contouring and Religrad for computing reliable gradients from scalar data. It also contains programs ... information, for generating regular grid samplings of scalar and gradient fields, for measuring the angle...

embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC). A nonlinear ... that is suitable for dynamical systems with sampling times in the (sub)millisecond range ... augmented Lagrangian formulation with a tailored gradient method for the inner minimization problem. The algorithm...

give an incremental algorithm based on gradient projection for efficiently solving this problem. The algorithm ... demonstrate the utility of our technique with sample data from a number of practical applications...

computed by a distributed preconditioned conjugate gradient method. We analyze its iteration complexity and communication ... where the n data points are i.i.d. sampled and when the regularization parameter scales...